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Researcher · Panasonic (United States) · United States

Denis Gudovskiy

Denis Gudovskiy publishes mostly in Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering, on topics such as Advanced Neural Network Applications, Domain Adaptation and Few-Shot Learning and Adversarial Robustness in Machine Learning.

ORCID 0000-0002-6829-6667 OpenAlex
World rank
#2,810,354
of 7,710,434 ranked researchers
Rank in United States
#815,036
of 1,627,098
Works
29
Citations
830
Citations per work
28.6
h-index
7
i10-index 7Top 65% in Artificial Intelligence
Denis Gudovskiy

What does Denis Gudovskiy research?

Denis Gudovskiy's work is mostly in Computer Science (78%), Engineering (12%).

Physical Sciences: 26 works · 90.3% of their workComputer Science: 23 works · 78.1% of their workArtificial Intelligence: 12 works · 41.5% of their workComputer Vision and Pattern Recognition: 6 works · 22.0% of their workComputer Networks and Communications: 2 works · 7.3% of their workSignal Processing: 2 works · 7.3% of their workEngineering: 4 works · 12.2% of their workElectrical and Electronic Engineering: 2 works · 8.5% of their workComputational Mechanics: 1 works · 3.7% of their workEverything else: 3 works · 9.7% of their workTHEIR WORKDenis Gudovskiy29works
Physical Sciences90%

Their six largest subfields, inside their fields and domains; the grey slice is everything else they publish. The interactive view splits every one of their works down to single topics.

The fields and subfields, with their numbers
    1. Computer Science2378.1%
    2. Engineering412.2%
  2. Everything else39.7%
  1. 1Artificial Intelligence42% of their works
  2. 2Computer Vision and Pattern Recognition22% of their works
  3. 3Electrical and Electronic Engineering8% of their works
  4. 4Computer Networks and Communications7% of their works
  5. 5Signal Processing7% of their works
  6. 6Computational Mechanics4% of their works

Research topics

Which keywords describe Denis Gudovskiy's research?

The keywords of their largest research topics: Deep Learning, Neural Networks, Image Classification, Meta-Learning, Semi-Supervised Learning, Convolutional Neural Networks, Image Recognition and Machine Learning.

Size is their works in the topics tagged with each word, from their 8 largest topics. Each links to the topic it comes from most. The words come from the keyword list OpenAlex gives each topic, not from the text of the papers, so a word can appear that none of the papers uses.

All 22 words, with their numbers
  1. Deep Learning245 topics
  2. Neural Networks133 topics
  3. Image Classification112 topics
  4. Meta-Learning112 topics
  5. Semi-Supervised Learning102 topics
  6. Convolutional Neural Networks81 topic
  7. Image Recognition81 topic
  8. Machine Learning82 topics
  9. Domain Adaptation71 topic
  10. Transfer Learning71 topic
  11. Adversarial Examples51 topic
  12. Robustness51 topic
  13. Anomaly Detection41 topic
  14. Hyperparameter Optimization41 topic
  15. Image Captioning41 topic
  16. Neural Machine Translation41 topic
  17. Noisy Labels41 topic
  18. Statistical Machine Translation41 topic
  19. Unsupervised41 topic
  20. Visual Question Answering41 topic
  21. Active Learning31 topic
  22. Gaussian Processes31 topic

Where does Denis Gudovskiy work?

Denis Gudovskiy's main affiliation in the publication record is Panasonic (United States), United States.

How many publications and citations does Denis Gudovskiy have?

OpenAlex counts 29 works and 830 citations for Denis Gudovskiy, an h-index of 7, ranking #2,810,354 of 7,710,434 researchers worldwide on the quality score.

Ranked on field-normalised citation excellence (50%), output (30%) and citations (20%). Counts come from OpenAlex author records, which occasionally merge different people who share a name or split one person into several.

Papers, co-authors, who cited this work and researchers on the nearest topics have tabs of their own, above. Is this your page? Claim it with your ORCID iD, or request a correction or removal.

Discussion

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